Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

Chunking methods

Chunking methods

The Chunking transformation offers different chunking methods that you can use to split text into chunks.
You can choose one of the following chunking methods to chunk one input string field at a time:
Sentence splitting
Split the text by sentences. Sentence splitting is useful for accurately analyzing and understanding text structure to summarize and analyze content. Sentence boundaries are detected by punctuation and through formal list items. For example, list items that begin with letters or numbers, such as
A)
or
1.
, are counted as individual sentences regardless of whether or not they have ending punctuation. Alternatively, list items that begin with bullet points or en dashes aren't automatically detected as individual sentences and are chunked based off of punctuation.
For example, the text "The quick brown fox jumps over the lazy dog. It is a sunny day." is split into the following sentence chunks:
"The quick brown fox jumps over the lazy dog.", "It is a sunny day."
Word splitting
Split the text based on a configured number of words. Word splitting is useful for preserving word meanings and relationships, like names or locations, without aligning to sentence boundaries. Set a word chunking limit that's likely to capture common phrases or full sentences.
For example, the text "The quick brown fox jumps over the lazy dog. It is a sunny day." is split into the following chunks of five words each:
"The quick brown fox jumps", "jumps over the lazy dog.", "dog. It is a sunny", "sunny day."
Fixed length character splitting
Split the text based on a configured fixed number of characters. This method might split text in the middle of sentences, but it preserves whole words. Fixed length character splitting is useful for scenarios when you want to ensure near constant chunk sizes.
For example, the text "The quick brown fox jumps over the lazy dog. It is a sunny day." is split into the following chunks of 10 characters each:
"The quick ", " quick ", " brown fox", "fox jumped", "jumped ", " over the ", " lazy dog.", "It is a ", " sunny day", "day."
Word splitting and fixed length character splitting create a 10 percent overlap between chunks to preserve whole words and sentences. The Chunking transformation rounds up when calculating the overlap. For example, 10 percent of a word chunking limit that's set to five words equals a 0.5 word overlap, so the transformation creates a one word overlap.

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